When there is at least one exceedance of \(v\)  we identify clusters of exceedances using the clustering algorithm
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [1]}, which recursively groups cells into distinct clusters based on adjacency to neighbouring cells, without the need to specify the number of clusters in advance. We then extract the largest of the identified clusters and define this by \(S_t\)  containing \(m_t\)  locations in 2-dimensional space such that
\(S_t=\lbrace s_{t,1},\hdots , s_{t,m_t}\rbrace  \in \mathbb {R}^2\) .
